# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import sys
import torch.onnx
import timm


def pth2onnx(output_file):
    model = timm.create_model('pnasnet5large', pretrained=True)
    model.eval()

    input_names = ["image"]
    output_names = ["class"]
    dynamic_axes = {'image': {0: '-1'}, 'class': {0: '-1'}}
    dummy_input = torch.randn(1, 3, 331, 331)
    torch.onnx.export(model,
    dummy_input, 
    output_file,
    input_names = input_names,
    dynamic_axes = dynamic_axes,
    output_names = output_names,
    opset_version=11, verbose=True)


if __name__ == '__main__':
    pth2onnx(sys.argv[1])